How Riseup Labs Achieved 98% Data Annotation Accuracy at Scale

Discover how Riseup Labs built a dedicated 200-person data annotation operation that delivered 98% accuracy, reduced turnaround time by 25%, and provided continuous 24/7 support for a global consumer goods company.

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Project Overview

Product

AI Training Data Annotation

Industry

Consumer Goods

Services Provided

Technologies

Data Annotation Platforms

Automation-Assisted Workflows

Quality Assurance Systems

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Summary

A global consumer goods company needed a reliable data annotation partner to support AI and machine learning initiatives across its consumer product portfolio.

The company worked with diverse datasets that included product images, textual content, packaging information, and customer reviews. These datasets needed to be annotated accurately and consistently so they could support AI model development, consumer research, and product innovation.

The scale and variety of the data created a significant operational challenge. The client needed to process large volumes of information while maintaining strict quality standards, meeting aggressive delivery schedules, and supporting ongoing operations without interruption.

Riseup Labs established a dedicated data annotation operation built around a team of 200 trained annotators. The engagement combined client-specific annotation guidelines, standardized operating procedures, multi-level quality assurance, automation-assisted workflows, and 24/7 operational support.

The result was a scalable annotation workflow that achieved 98% accuracy while improving turnaround time by 25%.

Key Results

200 dedicated annotators supporting ongoing data annotation operations
98% annotation accuracy across delivered datasets
25% faster turnaround time for annotation projects
24/7 operational support for continuous delivery and issue resolution
Standardized annotation processes supporting quality and consistency at scale

The Challenge

A large-scale annotation requirement involving diverse data types

The client’s AI, machine learning, and consumer research initiatives depended on large quantities of accurately labeled data.

However, the datasets were not limited to a single format. The annotation operation needed to process multiple types of information, including:

Product images
Text-based datasets
Packaging information
Product-related content
Customer reviews
Consumer insight data

Each dataset required annotators to understand different labeling requirements while following the client’s specific guidelines.

Without a structured annotation operation, maintaining quality across such diverse data types would have been difficult.

Accuracy was critical for AI and machine learning initiatives

The annotated datasets were intended to support AI model training and consumer research. This meant annotation errors could reduce the reliability of model outputs and affect the quality of downstream insights.

Annotation quality needed to remain consistent across different annotators, data formats, and project requirements.

The client needed more than a large workforce. It needed a structured quality assurance system capable of identifying inconsistencies, correcting errors, and maintaining high accuracy throughout the operation.

Large volumes created scalability challenges

The client required an annotation partner capable of handling substantial and ongoing data volumes.

The operation needed to scale without allowing quality to decline as workloads increased. This required sufficient workforce capacity, clear processes, standardized instructions, and effective workload management.

The client also needed the flexibility to address changing annotation requirements as new datasets and business priorities emerged.

Aggressive timelines affected product innovation

Annotation projects needed to be completed quickly enough to support ongoing AI development, consumer research, and product initiatives.

Lengthy annotation cycles could slow down model development and delay the insights required by internal product teams.

The client therefore needed an annotation process that could increase delivery speed without sacrificing accuracy.

Consistency was difficult to maintain across a large workforce

Large annotation teams can introduce differences in interpretation and output if instructions are unclear or quality reviews are inconsistent.

The client needed every annotator to follow the same standards, regardless of the dataset being processed or the time at which the work was completed.

Maintaining this consistency required structured training, documented procedures, and multiple levels of quality review.

Operations needed to continue around the clock

The annotation requirements were continuous rather than limited to a single short-term project.

The client needed dependable operational support, rapid issue resolution, and the ability to maintain delivery across different working hours.

A conventional daytime-only workflow would not provide the responsiveness or continuity required for the engagement.

The client needed more than additional annotation capacity. They needed a scalable, 24/7 operation that could process diverse, high-volume datasets while maintaining the accuracy and consistency required for reliable AI and machine learning.

Data Annotation Team Lead

The Solution

A dedicated data annotation operation built for scale, accuracy, and continuous delivery

Riseup Labs designed a data annotation operation around the client’s dataset requirements, quality expectations, and delivery targets.

The solution combined dedicated human resources with standardized processes, quality assurance systems, annotation technology, and continuous operational support.

1. A dedicated team of 200 trained annotators

Riseup Labs deployed a specialized team of 200 annotators to support the client’s ongoing data requirements.

Team members were trained to follow the annotation rules and quality expectations associated with the client’s consumer product datasets.

The dedicated-team model provided the workforce capacity needed to process large data volumes while maintaining familiarity with the client’s requirements over time.

Rather than repeatedly assembling temporary project teams, Riseup Labs established a stable annotation workforce capable of supporting continuous operations.

Result: The client gained access to 200 trained annotators who could handle ongoing, high-volume annotation requirements.

2. Client-specific annotation guidelines

Riseup Labs organized the annotation workflow around clearly defined guidelines tailored to the client’s datasets.

These guidelines helped annotators understand:

Which data elements needed to be labeled
How different categories should be interpreted
How ambiguous examples should be handled
What quality standards needed to be maintained
When an item should be escalated for review

Providing consistent guidance reduced subjective interpretation and helped the team produce more uniform outputs.

Result: Annotators followed a shared set of standards, improving consistency across different datasets and project requirements.

3. Multi-level quality assurance

Riseup Labs introduced multiple levels of quality review to verify annotation outputs before delivery.

The quality assurance process included precision checks designed to identify incorrect labels, inconsistent interpretations, and deviations from the established annotation guidelines.

Issues discovered during quality reviews could then be corrected and used to improve future annotation performance.

This created a continuous quality feedback loop rather than relying on a single final inspection.

Result: The annotation operation achieved 98% accuracy, providing reliable datasets for AI and machine learning initiatives.

4. Advanced annotation tools and automation-assisted workflows

Riseup Labs used proprietary annotation platforms alongside automation-assisted processes to improve operational efficiency.

The annotation tools helped organize data, assign tasks, manage workloads, and support quality reviews. Automation-assisted workflows reduced unnecessary manual effort while allowing trained annotators to remain responsible for decisions requiring human interpretation.

This combination of technology and human expertise allowed the operation to process data more efficiently without compromising quality.

Result: Annotation tasks moved through the workflow more efficiently, contributing to a 25% improvement in turnaround time.

5. Standardized operating procedures

Riseup Labs established documented standard operating procedures for the annotation operation.

The procedures created a repeatable workflow for:

Receiving datasets
Preparing annotation tasks
Assigning workloads
Applying annotation guidelines
Conducting quality checks
Escalating unclear data
Correcting identified issues
Preparing completed datasets for delivery

Standardization helped ensure that work was completed consistently regardless of dataset type, annotator, or operating shift.

The procedures were also reviewed and optimized as the operation evolved.

Result: The client received more consistent outputs through a scalable and repeatable annotation process.

6. Continuous process optimization

Riseup Labs monitored annotation performance and used operational findings to improve the workflow continuously.

Quality findings, recurring annotation questions, and workflow bottlenecks were used to refine instructions and strengthen the annotation process.

This allowed the operation to adapt as requirements changed without rebuilding the workflow for every new dataset.

Result: The annotation process became more efficient and scalable while continuing to meet the client’s quality requirements.

7. Around-the-clock operational support

Riseup Labs maintained 24/7 operations to support continuous annotation delivery and issue resolution.

The operational model allowed work to progress across different shifts while ensuring that questions, technical issues, and delivery requirements could be addressed promptly.

Structured handovers and standardized procedures helped maintain consistency between operating shifts.

Result: The client gained uninterrupted annotation support and greater responsiveness across ongoing projects.

Before vs. After Working with Riseup Labs BPO

Before Riseup Labs

Goal: Process high-volume datasets for AI, machine learning, and consumer research
Manage large volumes of images, text, packaging information, and customer review data
Maintain strict annotation accuracy across diverse data formats and project requirements
Meet aggressive turnaround times while supporting ongoing product and AI initiatives
Keep annotation outputs consistent across large teams and multiple operating shifts
Scale annotation capacity without building and managing a large internal workforce
Provide continuous operational coverage and resolve annotation issues quickly

Result: Slower annotation delivery, inconsistent outputs, and high operational complexity
Visibility: Limited oversight across annotation quality, workforce capacity, and project progress

After Riseup Labs

Goal: Build a scalable, accurate, and continuously available data annotation operation
Deploy a dedicated team of 200 trained annotators for ongoing annotation requirements
Achieve 98% annotation accuracy through multi-level quality assurance and precision checks
Improve turnaround time by 25% using standardized and automation-assisted workflows
Follow client-specific guidelines and SOPs to maintain consistency across datasets and shifts
Use proprietary annotation platforms to manage workloads, reviews, and delivery efficiently
Provide 24/7 operational support for uninterrupted annotation and issue resolution

Result: Faster annotation delivery, reliable AI training data, and scalable operational capacity
Visibility: Centralized oversight across annotation quality, team performance, workflows, and project delivery

Impact

Reliable training data for AI and machine learning

The 98% annotation accuracy rate provided the client with dependable datasets for AI model development.

By reducing annotation errors and inconsistencies, Riseup Labs helped strengthen the quality of the data being used for model training and consumer analysis.

Faster annotation delivery

The combination of a dedicated workforce, standardized workflows, annotation technology, and automation assistance reduced turnaround time by 25%.

Faster delivery helped the client move annotated data into AI development, research, and product workflows more quickly.

Greater annotation capacity

The dedicated team of 200 annotators gave the client the capacity to process large and ongoing data volumes.

This allowed annotation activities to scale without requiring the client to recruit, train, and manage a large internal annotation workforce.

Improved consistency across datasets

Client-specific guidelines and standard operating procedures created a shared framework for processing different data types.

This reduced variation between annotators and helped maintain consistent outputs across images, text, packaging data, and customer reviews.

Continuous operational availability

The 24/7 operating model allowed annotation work to continue without being limited to standard business hours.

It also enabled quicker issue resolution and helped the client maintain progress across ongoing projects.

Stronger support for product innovation

Faster access to accurately annotated data helped support the client’s AI initiatives, consumer research, and product development activities.

The improved annotation operation gave internal teams a more reliable data foundation for developing models and generating insights.

The Business Value

The engagement gave the client more than additional annotation capacity. It created a scalable operating model for producing accurate AI training data consistently and efficiently.

The business value was delivered across five key areas.

Accuracy

Multi-level quality assurance helped the operation achieve 98% annotation accuracy, strengthening confidence in the datasets used for AI and machine learning.

Speed

Automation-assisted workflows, dedicated resources, and standardized procedures reduced annotation turnaround time by 25%.

Scale

A dedicated team of 200 trained annotators provided the workforce capacity required to support high-volume and ongoing annotation projects.

Consistency

Client-specific guidelines and standard operating procedures helped maintain uniform annotation standards across data types, projects, annotators, and operating shifts.

Operational continuity

Round-the-clock support allowed annotation activities and issue resolution to continue 24/7, reducing interruptions to project delivery.

Conclusion

Riseup Labs helped a global consumer goods company build a scalable, accurate, and continuously available data annotation operation.

By combining a dedicated team of 200 trained annotators, client-specific annotation guidelines, multi-level quality assurance, proprietary annotation technology, automation-assisted workflows, standardized procedures, and 24/7 operational support, Riseup Labs created a dependable foundation for ongoing AI and machine learning initiatives.

The engagement achieved 98% annotation accuracy while improving turnaround time by 25%.

The result was more than an outsourced annotation team. It was a structured data operation that helped the client process diverse datasets efficiently, support AI-driven innovation, and scale annotation capacity with confidence.

Scale Your Data Annotation Operations with Confidence

Our dedicated teams can support image annotation, text annotation, product data labeling, customer review analysis, quality assurance, and continuous data operations.

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